Current Research

Dr. Liu’s statistical methodological research focuses on the development and application of 1) modern approaches for protecting data privacy. Some recent work involves integrating the concept of differential privacy and the data synthesis techniques in the framework of statistical disclosure limitation; 2) Statistical learning in big data. Some recent work includes Gaussian Graphical model estimation and differentiation, and regularization of deep learning in neural networks; 3) Bayesian models for correlated and clustered data originated from medical, biological, and social sciences. Recent work focuses on Bayesian models of non-Gaussian repeated measures; 4) Missing data analysis techniques and concepts; One recent work proposes a Bayesian method dealing with not-missing-at-random mechanism in meta-analysis; 5) Biostatistical and epidemiological applications. Most of the recent work is motivated by Dr. Liu's collaboration with epidemiologists and entomologists in two Gates-funded malaria prevention studies.

Among the first group of statisticians who came to the newly formed ACMS department since 2011, Dr. Liu has also brought her statistical expertise and rich consulting experience to various collaborations both on campus and with external organizations. Dr. Liu has served as the statistician on multiple Gates Foundation funded projects to assess the efficacy of promising malaria control methods by collaborating with the research team to optimize the study design and prepare the statistical analysis plan to ensure the efficiency and validity of final data analysis. Dr. Liu also serves as the Notre Dame Liaison on Design and Biostatistics Program under the Indiana Clinical and Translational Sciences Institute to provide investigators centralized access to the biostatistics and bioinformatics programs among the four Indiana Research Universities.